Speech recognition systems use grammars to constrain the space of possible spoken utterances. In most cases, these grammars are built well before deployment and are carefully tuned to achieve best performance in the general case and for a general class of users. However, it is often the case that the application gains contextual information about the specific user, the specific scenario, the specific environment, or other specific information of value at deployment time which could be leveraged in the grammar. Moreover, a user typically has valuable information that is not known until the system is deployed and running. This late-binding effect suggests technology to support parsimonious integration of application-dependent information into the general grammar at a desired time.